We follow the literature [1]}, [2]}, [3]}, [4]} to assume no prior knowledge about the reward function and thus use neural network to approximate transition dynamic and the reward function.
Our model is constructed as an ensemble of Gaussian probabilistic networks .
Except for the weighted loss function, we use the same model architecture, ensemble size (=7), number of elite model (=5), train-test set split, optimization scheme, elite-model selection criterion, and sampling method as in [3]}.
To save computation, we define each epoch of model training as 1000 mini-batch gradient steps, instead of the original n_train / batch_size.
